An Observation Capability Metadata Model for EO Sensor Discovery in Sensor Web Enablement Environments

Remote Sensing

Chuli Hu²  Qingfeng Guan²  Nengcheng Chen³  Jia Li³  Xiang Zhong⁴  Yongfei Han⁵  Gonzalo Pajares Martinsanz¹ 

[1] National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China; E-Mail;National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China; E-Mail:;State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; E-Mail:;Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China; E-Mail:;Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China; E-Mail:

关键词: Earth observation sensor web;    collaborative observation;    metadata;    discovery;    observation capability;   

DOI  :  10.3390/rs61110546

来源: mdpi

【 摘 要 】

Accurate and fine-grained discovery by diverse Earth observation (EO) sensors ensures a comprehensive response to collaborative observation-required emergency tasks. This discovery remains a challenge in an EO sensor web environment. In this study, we propose an EO sensor observation capability metadata model that reuses and extends the existing sensor observation-related metadata standards to enable the accurate and fine-grained discovery of EO sensors. The proposed model is composed of five sub-modules, namely, ObservationBreadth, ObservationDepth, ObservationFrequency, ObservationQuality and ObservationData. The model is applied to different types of EO sensors and is formalized by the Open Geospatial Consortium Sensor Model Language 1.0. The GeosensorQuery prototype retrieves the qualified EO sensors based on the provided geo-event. An actual application to flood emergency observation in the Yangtze River Basin in China is conducted, and the results indicate that sensor inquiry can accurately achieve fine-grained discovery of qualified EO sensors and obtain enriched observation capability information. In summary, the proposed model enables an efficient encoding system that ensures minimum unification to represent the observation capabilities of EO sensors. The model functions as a foundation for the efficient discovery of EO sensors. In addition, the definition and development of this proposed EO sensor observation capability metadata model is a helpful step in extending the Sensor Model Language (SensorML) 2.0 Profile for the description of the observation capabilities of EO sensors.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.